Ontology Matching: A Machine Learning Approach

Doan, AnHai; Madhavan, Jayant; Domingos, Pedro; Halevy, Alon
Doan, A
Madhavan, J
Domingos, P
Halevy, A
Handbook on Ontologies in Information Systems
Citations range: 
100 - 499

This chapter studies ontology matching: the problem of finding the seman-
tic mappings between two given ontologies. This problem lies at the heart of
numerous information processing applications. Virtually any application that
involves multiple ontologies must establish semantic mappings among them,
to ensure interoperability. Examples of such applications arise in myriad do-
mains, including e-commerce, knowledge management, e-learning, information
extraction, bio-informatics, web services, and tourism (see Part D of this book
on ontology applications).
Despite its pervasiveness, today ontology matching is still largely con-
ducted by hand, in a labor-intensive and error-prone process. The manual
matching has now become a key bottleneck in building large-scale informa-
tion management systems. The advent of technologies such as the WWW,
XML, and the emerging Semantic Web will further fuel information sharing
applications and exacerbate the problem. Hence, the development of tools to
assist in the ontology matching process has become crucial for the success of
a wide variety of information management applications.
In response to the above challenge, we have developed GLUE, a system that
employs learning techniques to semi-automatically create semantic mappings
between ontologies. We shall begin the chapter by describing a motivating ex-
ample: ontology matching on the Semantic Web. Then we present our GLUE
solution. Finally, we describe a set of experiments on several real-world do-
mains, and show that GLUE proposes highly accurate semantic mappings.